Quest_SA: Preprocessing Method for Closed-Ended Questionnaires Using Sentiment Analysis through Polarity

Table 5

Sample code of SentimentAnalyzer.

---

/∗

---

To change this license header, choose License Headers in Project Properties.

To change this template file, choose Tools | Templates

and open the template in the editor.

/

package servlet1;

import java.util.Properties;

import org.ejml.simple.SimpleMatrix;

import edu.stanford.nlp.ling.CoreAnnotations;

import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations;

import edu.stanford.nlp.pipeline.Annotation;

import edu.stanford.nlp.pipeline.StanfordCoreNLP;

import edu.stanford.nlp.sentiment.SentimentCoreAnnotations;

import edu.stanford.nlp.trees.Tree;

import edu.stanford.nlp.util.CoreMap;

/

@author jayanthi

/

public class SentimentAnalyzer

{

static Properties props;

static StanfordCoreNLP pipeline;

public void initialize(String path)

{

// creates a StanfordCoreNLP object, with POS tagging, lemmatization, NER, parsing, and sentiment

props = new Properties();

props.setProperty(“parse.model”, path+”edu\\stanford\\nlp\\models\\lexparser\\englishPCFG.ser.gz”);

props.setProperty(“sentiment.model”, path+”edu\\stanford\\nlp\\models\\sentiment\\sentiment.ser.gz”);

props.setProperty(“annotators”, “tokenize, ssplit, parse, sentiment”);

pipeline = new StanfordCoreNLP(props);

//LexicalizedParser lp = LexicalizedParser.loadModel(“edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz”);

}

public SentimentResult getSentimentResult(String text) {

SentimentResult sentimentResult = new SentimentResult();

SentimentClassification sentimentClass = new SentimentClassification();

if (text ! = null && text.length() > 0) {

// run all Annotators on the text

Annotation annotation = pipeline.process(text);

for (CoreMap sentence: annotation.get(CoreAnnotations.SentencesAnnotation.class)) {

// this is the parse tree of the current sentence

Tree tree = sentence.get(SentimentCoreAnnotations.SentimentAnnotatedTree.class);

SimpleMatrix sm = RNNCoreAnnotations.getPredictions(tree);

String sentimentType = sentence.get(SentimentCoreAnnotations.SentimentClass.class);

sentimentClass.setVeryPositive((double)Math.round(sm.get(4) 100d));

sentimentClass.setPositive((double)Math.round(sm.get(3) 100d));

sentimentClass.setNeutral((double)Math.round(sm.get(2) 100d));

sentimentClass.setNegative((double)Math.round(sm.get(1) 100d));

sentimentClass.setVeryNegative((double)Math.round(sm.get(0) 100d));

sentimentResult.setSentimentScore(RNNCoreAnnotations.getPredictedClass(tree));

sentimentResult.setSentimentType(sentimentType);

sentimentResult.setSentimentClass(sentimentClass);

}

}

Return sentimentResult;

}

}

---